ToAPIs

Краткий вывод

  • Official capabilities include a 1M context window, up to 64K output, plus function calling, structured output, search tools, and code execution.
  • Compared with classic lightweight Flash tiers, it is better suited to complex Q&A, coding, long-document handling, and agent workflows.
  • The recommended integration path remains OpenAI-compatible chat so existing SDKs, SSE streaming, and retry middleware can be reused.

Частые ошибки

  • 400 invalid_request_error: триггер=Missing required fields, malformed messages, or invalid parameter types in the payload.; исправление=Validate model, messages, max_tokens, and any tools/schema JSON before retrying.; повтор=Retry only after fixing the payload; avoid blind retries.
  • 401 authentication_error: триггер=Missing Authorization header, malformed bearer token, or invalid API key.; исправление=Verify Authorization: Bearer <YOUR_API_KEY> format and key validity.; повтор=Retry after authentication is fixed.
  • 429 rate_limit_error: триггер=Request rate, concurrency, or quota usage has hit upstream rate limiting.; исправление=Apply exponential backoff and inspect concurrency, context size, and current quota consumption.; повтор=Use 1s/2s/4s backoff with jitter; reduce concurrency or downgrade workload shape if it persists.
  • 500 internal_error: триггер=Transient upstream instability, tool execution failure, or internal processing issues.; исправление=Capture request id and a compact context summary, then retry; escalate if failures persist.; повтор=Retry 2-3 times with short delays.
Назад к рынку моделей

Разбор модели

gemini-3.5-flash

gemini-3.5-flash is Google's next-generation Flash text model released on May 19, 2026, positioned around higher intelligence with Flash-class latency. It fits high-throughput chat, coding copilots, long-context Q&A, structured output, and tool-enabled workflows through ToAPIs-compatible chat integration.

Вендор

Google (Gemini)

Модальности

Image

Цена

Input 90 credits/1M, Output 540 credits/1M

Обновлено

2026-05-21

Открыть в PlaygroundДокументация

Обзор модели

Quick Answer

  • Official capabilities include a 1M context window, up to 64K output, plus function calling, structured output, search tools, and code execution.
  • Compared with classic lightweight Flash tiers, it is better suited to complex Q&A, coding, long-document handling, and agent workflows.
  • The recommended integration path remains OpenAI-compatible chat so existing SDKs, SSE streaming, and retry middleware can be reused.

gemini-3.5-flash Возможности модели

Ключевой блок

Ключевые возможности и практическая ценность

High intelligence with Flash latency

It is not just a low-cost fallback tier; it aims to handle deeper understanding, planning, and generation while staying responsive.

1M long context

Well suited to long-document summarization, codebase analysis, knowledge synthesis, large prompts, and extended conversations.

Structured and tool-based output

Function calling, structured JSON, search tools, and code execution make it a stronger fit for agentic workflows.

Coding and technical generation

Useful for code explanation, unit test generation, refactor proposals, SDK wrappers, and technical drafts.

OpenAI-compatible migration path

Chat Completions-style payloads reduce switching friction from GPT- or Claude-oriented stacks.

Streaming interaction

SSE streaming supports chat UIs, terminal assistants, and IDE-style progressive rendering.

Как использовать API gemini-3.5-flash

  1. Create an API key and set Authorization: Bearer <YOUR_API_KEY>.
  2. POST to /v1/chat/completions with at least model and messages.
  3. Tune max_tokens, temperature, and top_p based on task complexity and determinism needs.
  4. Enable stream=true for real-time UX, or add tools and tool_choice for tool-enabled workflows.
  5. Use finish_reason, usage, and application logs to refine prompts and output structure before broader rollout.

Common Errors

400 invalid_request_error

Trigger: Missing required fields, malformed messages, or invalid parameter types in the payload.

Fix: Validate model, messages, max_tokens, and any tools/schema JSON before retrying.

Retry: Retry only after fixing the payload; avoid blind retries.

401 authentication_error

Trigger: Missing Authorization header, malformed bearer token, or invalid API key.

Fix: Verify Authorization: Bearer <YOUR_API_KEY> format and key validity.

Retry: Retry after authentication is fixed.

429 rate_limit_error

Trigger: Request rate, concurrency, or quota usage has hit upstream rate limiting.

Fix: Apply exponential backoff and inspect concurrency, context size, and current quota consumption.

Retry: Use 1s/2s/4s backoff with jitter; reduce concurrency or downgrade workload shape if it persists.

500 internal_error

Trigger: Transient upstream instability, tool execution failure, or internal processing issues.

Fix: Capture request id and a compact context summary, then retry; escalate if failures persist.

Retry: Retry 2-3 times with short delays.

FAQ

What is gemini-3.5-flash best for?

It is best for text and coding assistant tasks that need strong intelligence without giving up throughput and response speed.

How is it different from a classic Flash model?

The main difference is a higher intelligence ceiling, making it more suitable for complex Q&A, long context, and tool-enabled workflows.

What is the fastest way to integrate it?

Use the OpenAI-compatible Chat Completions path first so you can reuse existing messages, streaming, and retry middleware.

When should max_tokens be constrained carefully?

Constrain max_tokens carefully for long-context, coding, and structured-output tasks to avoid bloated responses, cost drift, or timeouts.

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